Study on Defection Segmentation for Steel Surface Image Based on Image Edge Detection and Fisher Discriminant

A hybrid image segmentation method based on edge detection and Fisher discriminant is presented to detect defection, because signal-to-noise ratio of steel surface image is very low, and defection targets are small and their shape is irregular. Firstly, gradient operator detects the edge of defectio...

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Veröffentlicht in:	Journal of physics. Conference series 2006-10, Vol.48 (1), p.364-368
Hauptverfasser:	Guo, J H, Meng, X D, Xiong, M D
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Sprache:	eng
Schlagworte:	Edge detection Image contrast Image enhancement Image segmentation Morphology Physics Signal to noise ratio
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creator	Guo, J H Meng, X D Xiong, M D
description	A hybrid image segmentation method based on edge detection and Fisher discriminant is presented to detect defection, because signal-to-noise ratio of steel surface image is very low, and defection targets are small and their shape is irregular. Firstly, gradient operator detects the edge of defection image and gradient image is gotten, then grayscale of gradient image is stretched in order to enhance image contrast. Secondly, Fisher discriminant is adopted in order to find optimum threshold, meanwhile defection targets are segmented. Lastly, noise is filtered by morphology method. Defection is auto-segmented and located by this segmentation method. Experiment results show this method can detect week defection and real-time detect defection online.
doi_str_mv	10.1088/1742-6596/48/1/068
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subjects	Edge detection Image contrast Image enhancement Image segmentation Morphology Physics Signal to noise ratio
title	Study on Defection Segmentation for Steel Surface Image Based on Image Edge Detection and Fisher Discriminant
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